TensorFlow 2.20 with Python 3.13.5 – keras import errors and model.predict failure in C2_W1_Lab01

Course Q&A Machine Learning Specialization Advanced Learning Algorithms
1

Hi,

While trying to implement the code C2_W1_Lab01, I ran into multiple errors with the TensorFlow package.

  • My setup:

    • Python 3.13.5 (Linux)

    • TensorFlow 2.20.0 (installed via pip in a fresh venv)

  • The issues:

    1. The code provided in the lab tries to import keras using commands such as from tensorflow.keras.layers import Dense , but with TF 2.20 this leads to errors.

    2. I tried to add .python as from tensorflow.python.keras.layers import Dense, which gave no error initially. However, later when I tried model.predict() function, it gave the following error: AttributeError: module 'tensorflow.python.distribute.input_lib' has no attribute 'DistributedDatasetInterface

Question:

  • Is TensorFlow 2.20 fully supported on Python 3.13.5, or should I downgrade to Python 3.12 to avoid these issues?

  • Has anyone encountered this DistributedDatasetInterface error when using model.predict? If so, what’s the recommended fix?

Any guidance on how to resolve this and properly run the lab code would be appreciated.

Thanks,
Mohamed Tolba

1 Like
2

Hello Mohamed Tolba,

All labs are designed for running in the Coursera environment and we don’t have resources like guidance to run them in any computer. I generally recommend learners to either work on them on Coursera or use them in view-only mode as a reference for building their own code, and these are also my recommendations for you.

However, if you are particularly interested in understanding these Tensorflow matters, I would suggest you to bring the questions to StackExchange or the Tensorflow community (go to here and click “Forum”) for a wider audience with Tensorflow expertise.

Good luck!
Raymond

3

Hi Raymond,

Thank you for your reply.

I’m actually using the labs as a reference to help me build my own code. However, the code provided in the labs doesn’t seem to work with me when I use the newer versions of TensorFlow. Do you know which TensorFlow version the course labs were built and tested on?

Thanks

1 Like
4

You may check it on Coursera by going to the lab and run import tensorflow as tf followed by print(tf.__version__).

Besides, why don’t you just build a neural network from scratch in your environment with your Tensorflow? Even though the lab doesn’t give you the exact code that can run in your environment, as a reference, it should at least give you some idea of how it should be like, and then you can work the rest out on your own or with the help of some online tutorials. Tensorflow has many tutorials and they are just a google search away.

2 Likes